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Title: miRNA detection and analysis from high-throughput small RNA sequencing data
Author: Paicu, Claudia
ISNI:       0000 0004 6351 0358
Awarding Body: University of East Anglia
Current Institution: University of East Anglia
Date of Award: 2016
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Small RNAs (sRNAs) are a broad class of short regulatory non-coding RNAs. microRNAs (miRNAs) are a special class of -21-22 nucleotide sRNAs which are derived from a stable hairpin-like secondary structure. miRNAs have critical gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in both plants and animals. Next generation sequencing (NGS) technologies, which are often used for identifying miRNAs, are continuously evolving, generating datasets containing millions of sRNAs, which has led to new challenges for the tools used to predict miRNAs from such data. There are several tools for miRNA detection from NGS datasets, which we review in this thesis, identifying a number of potential shortcomings in their algorithms. In this thesis, we present a novel miRNA prediction algorithm, miRCat2. Our algorithm is more robust to variations in sequencing depth due to the fact that it compares aligned sRNA reads to a random uniform distribution to detect peaks in the input dataset, using a new entropy-based approach. Then it applies filters based on the miRNA biogenesis on the read alignment and on the computed secondary structure. Results show that miRCat2 has a better specificity-sensitivity trade-off than similar tools, and its predictions also contains a larger percentage of sequences that are downregulated in mutants in the miRNA biogenesis pathway. This confirms the validity of novel predictions, which may lead to new miRNA annotations, expanding and contributing to the field of sRNA research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available